PSXI-13 Effect of Lactobacillus spp. and Saccharomyces cerevisiae alone or in combination, on ruminal fermentation, total tract nutrient digestibility and performance of growing beef cattle
Bibliographic record
Abstract
Abstract The study evaluated the effect of Saccharomyces cerevisiae alone or in combination with Lactobacillus spp. delivered through silages as a direct fed microbial (DFM) on the nutrient digestibility and performance of growing beef cattle. Chopped and kernel processed corn silage was treated with either distilled water (CON), or with (cfu g1 fresh forage) 1.1 × 105 of a Lactobacillius plantarum and Lactobacillus buchneri (LAB) mixture (INOC1), 1.0 × 104 S. cerevisiae strain 3 (INOC2), or 1.1 × 105 LAB + 1.0 × 104 S. cerevisiae strain 3 (INOC3) and ensiled in AgBags®. Eight ruminally cannulated beef heifers in a replicated 4 × 4 Latin square and 60 crossbred individually fed beef steers in a randomized complete block design were used for the metabolism and feedlot growth performance experiments, respectively. The treatments were four total mixed rations composed of 65.0% of one of the four corn silages, 17.0% barley grain, 13.0% canola meal and 5.0% vitamin-mineral supplement (dry matter (DM) basis) for both experiments. Data were analyzed using the MIXED procedure of SAS. The molar percentage of ruminal acetate in heifers fed INOC2 and INOC3 were lower (P < 0.01) than those fed CON. The DM (P = 0.03), organic matter (P = 0.02), acid (P = 0.03) and neutral detergent fiber (P = 0.03) digestibility of heifers fed INOC2 were higher than those fed CON. The DM intake as a % of body weight tended (P = 0.08) to be lower for steers fed INOC2, while the average daily gain was similar to those fed CON. There is potential for delivering S. cerevisiae through silage as DFM for improving ruminal fermentation, total tract nutrient digestibility and the growth performance of beef cattle.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".